Delay-dependent robust stability for uncertain stochastic fuzzy Hopfield neural networks with time-varying delays

被引：29
|
作者：
Sheng, Li [1 ,3 ]
Gao, Ming [2 ]
Yang, Huizhong [1 ]
机构：
[1] Jiangnan Univ, Sch Commun & Control Engn, Wuxi 214122, Jiangsu, Peoples R China
[2] Shandong Univ Sci & Technol, Coll Informat & Elect Engn, Qingdao 266510, Peoples R China
[3] Univ Maryland, Syst Res Inst, College Pk, MD 20742 USA
来源：
FUZZY SETS AND SYSTEMS | 2009年 / 160卷 / 24期
基金：
中国国家自然科学基金;
关键词：
Delay-dependent robust stability; Fuzzy systems; Hopfield neural networks; Stochastic systems; Time-varying delays; EXPONENTIAL STABILITY; ASYMPTOTIC STABILITY; DISTRIBUTED DELAYS; SYSTEMS; PERIODICITY; DISCRETE;
D O I：
10.1016/j.fss.2009.09.015
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
Takagi-Sugeno (TS) fuzzy models are often used to represent complex nonlinear systems by means of fuzzy sets and fuzzy reasoning applied to a set of linear sub-models. In this paper, the global robust stability problem of TS fuzzy Hopfield neural networks with parameter uncertainties and stochastic perturbations is investigated. Based on the Lyapunov method and stochastic analysis approaches, the delay-dependent stability criterion is derived in terms of linear matrix inequalities (LMIs), which can be solved efficiently by using existing LMI optimization techniques. A simulation example is provided to illustrate the effectiveness of the developed method. (C) 2009 Published by Elsevier B.V.
引用
收藏
页码：3503 / 3517
页数：15
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